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In-Situ Photoluminescence for Perovskite Crystallization: Bridging Mechanistic Insights and Device Engineering
Haoran Yang1, Hu Guo1, Yunfan Wang2
1School of New Energy, Nanjing University of Science and Technology, Jiangyin, Jiangsu, China.
Advanced Materials (Deerfield Beach, Fla.)
|February 9, 2026
Summary
In-situ photoluminescence (PL) spectroscopy tracks perovskite film crystallization in real-time, crucial for high-efficiency solar cells. This technique aids in optimizing manufacturing for stable, commercial perovskite photovoltaics.
Area of Science:
- Materials Science
- Photovoltaics
- Spectroscopy
Background:
- Metal halide perovskite solar cells achieve high efficiencies but suffer from performance and stability issues linked to film crystallization.
- Traditional characterization methods fail to capture dynamic crystallization processes, hindering optimization.
- In-situ photoluminescence (PL) spectroscopy offers a non-invasive method for real-time monitoring of perovskite film formation.
Purpose of the Study:
- To review advancements in in-situ PL spectroscopy for studying perovskite nucleation and crystallization.
- To highlight the application of in-situ PL in understanding crystallization pathways and the influence of processing parameters.
- To explore future directions, including machine learning integration for process optimization.
Main Methods:
- Review of recent literature on in-situ PL instrumentation (single-point, multi-probe, imaging).
- Analysis of studies utilizing in-situ PL to investigate crystallization in various perovskite compositions.
- Discussion of complementary techniques integrated with in-situ PL.
Main Results:
- In-situ PL effectively tracks nucleation, crystal growth, phase transitions, and defect evolution in real-time.
- The technique reveals the impact of antisolvents, additives, and interfacial engineering on perovskite film quality.
- Integration with other methods provides a comprehensive understanding of crystallization mechanisms.
Conclusions:
- In-situ PL spectroscopy is a vital tool for elucidating perovskite crystallization dynamics.
- Optimizing processing conditions using in-situ PL insights can enhance photovoltaic performance and stability.
- Combining in-situ PL with machine learning promises accelerated development and scalable manufacturing of perovskite solar cells.
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